Vision and Mission of the Department

Vision

To bring forth technically versatile, Research oriented, Industry ready engineers in the field of Artificial Intelligence and Machine Learning.

Mission

Facilitate modern infrastructure and versatile learning resources to produce self-sustainable professionals
Facilitate project based learning and skill upgradation through industry collaborations
Inculcate professional ethics, leadership qualities and practice lifelong learning

Program Specific Outcomes (PSOs):

PSO1: Graduates will have the ability to adapt, contribute and innovate ideas in the field of Artificial Intelligence and Machine Learning.
PSO2: To provide a concrete foundation and enrich their abilities to qualify for Employment, Higher studies and Research in various domains of Artificial Intelligence and Machine Learning such as Data Science, Computer Vision, Natural Language Processing with Ethical Values.
PSO3: Graduates will acquire the practical proficiency with niche technologies and open-source platforms and to become Entrepreneur in the domain Artificial Intelligence and Machine Learning.

Program Educational Objectives (PEOs):

PEO1: Attain proficiency in professional practice
PEO2: Practice technical skills to identify, analyze and solve complex problems related to Artificial Intelligence and Machine Learning.
PEO3: Emerge as an Individual or a team member with societal concerns, ethics and motivated for holistic learning.

About the Course

Application of Data Structures

Data structures are used in various fields such as:

Operating system
Graphics
Computer Design
Blockchain
Genetics
Image Processing
Simulation
etc.

A data structure is a group of data elements that provides the easiest way to store and perform different actions on the data of the computer. A data structure is a particular way of organizing data in a computer so that it can be used effectively. The idea is to reduce the space and time complexities of different tasks.

A data structure has also defined an instance of ADT (ABSTRACT DATA TYPE). It is formally defined as a triplet:

[D,F,A], where

D: Set of the domain.
F: the set of operations.
A: the set of axioms.

Need Of Data structure:

The structure of the data and the synthesis of the algorithm are relative to each other. Data presentation must be easy to understand so the developer, as well as the user, can make an efficient implementation of the operation.

Data structures provide an easy way of organizing, retrieving, managing, and storing data.

Here is a list of the needs for data.

Data structure modification is easy.
It requires less time.
Save storage memory space.
Data representation is easy.
Easy access to the large database

Syllabus

Module 1

INTRODUCTION TO DATA STRUCTURES: Data Structures, Classifications (Primitive & Non-Primitive), Data structure Operations Review of pointers and dynamic Memory Allocation,

ARRAYS and STRUCTURES: Arrays, Dynamic Allocated Arrays, Structures and Unions, Polynomials, Sparse Matrices, representation of Multidimensional Arrays, Strings

STACKS: Stacks, Stacks Using Dynamic Arrays, Evaluation and conversion of Expressions

Module 2

QUEUES: Queues, Circular Queues, Using Dynamic Arrays, Multiple Stacks and queues.

LINKED LISTS : Singly Linked, Lists and Chains, Representing Chains in C, Linked Stacks and Queues, Polynomials

Module 3

LINKED LISTS : Additional List Operations, Sparse Matrices, Doubly Linked List.

TREES: Introduction, Binary Trees, Binary Tree Traversals, Threaded Binary Trees.

Module 4

TREES(Cont..): Binary Search trees, Selection Trees, Forests, Representation of Disjoint sets, Counting Binary Trees,

GRAPHS: The Graph Abstract Data Types, Elementary Graph Operations

Module 5

HASHING: Introduction, Static Hashing, Dynamic Hashing